Point-based object detectors, such as PointRCNN [1]}, 3DSSD [2]}, and Point-GNN [3]}, extract discriminative features from raw point clouds with PointNet [4]} and its improved version PointNet++ [5]} and generate proposals centered at each point with 3D region proposal network. The drawback of point-based object detectors is the demand for the high computation cost. To address this problem, voxel-based detectors [6]}, [7]}, [8]}, [9]}, [10]} transform the irregular point clouds into the volumetric representations. The pioneer VoxelNet [6]} and its follower SECOND [7]} apply the 3D CNNs and the Sparse 3D CNNs to regularize the 4D voxels. PointPillars[8]} takes advantage of a standard 2D convolutional detection pipeline which codes point clouds into a special partition of voxels (i.e., pillars) in the light of the tradeoff between efficiency and accuracy. However, the step of point clouds discretization in voxel-based detectors will degrade the fine-grained localization accuracy [14]}. Some point-voxel-based methods [15]}, [16]}, [17]}, [18]} have been proposed, which take the advantage of localization accuracy of point-based detectors and computational efficiency of voxel-based detectors. PV-RCNN [15]} first utilizes 3D sparse CNNs to encode the whole scene and then aggregates the multi-scale semantic voxel-wise features from 3D CNNs into keypoint features for localization refinement. How to design the pre-training network to improve the 3D detection performance has rarely been studied.
